Computer networks, such as public networks (e.g., the Internet) and private networks (e.g., at medical institutions, financial institutions, business enterprises, etc.) have become a medium for research, communication, distribution, and storage of data. Consequently, more and more devices are network-enabled. To illustrate, on any given day, a typical user may access a half-dozen or more network-enabled devices, such as their mobile phone, tablet computer, home security system devices, one or more wearable devices, a home laptop or desktop, a work laptop or desktop, and home entertainment devices (e.g., televisions, game consoles, set top boxes, etc.). Moreover, Internet of Things (IoT) protocols enable network-enabled devices to communicate with each other without user intervention. Thus, there is an increasing amount of data being accessed, transferred, and stored online. As users use networks to access data, they also generate a large amount of data regarding themselves. On websites such as social networks, users actively and willingly share data regarding themselves. Thus, at any given time, different subsets of data regarding a user or a group of users may be available online. However, it may be difficult to share data across data aggregators or websites to gain a more “complete” understanding of a user due to privacy concerns and/or technological incompatibilities (e.g., incompatible data storage formats across different aggregators or websites). In addition, data sharing may be limited to an “all-or-nothing” model, i.e., a website or aggregator may have only two choices regarding data sharing: share all data regarding all users, or share no data regarding any user.